{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EheA5_j_cEwc"
      },
      "source": [
        "##### Copyright 2019 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "YCriMWd-pRTP"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OvRwFTkqcp1e"
      },
      "source": [
        "# Interest rate tools in TFF\n",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/google/tf-quant-finance/blob/master/tf_quant_finance/examples/jupyter_notebooks/Cashflows_Rate_Curves.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/google/tf-quant-finance/blob/master/tf_quant_finance/examples/jupyter_notebooks/Cashflows_Rate_Curves.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "uG8UAZXjeUhf"
      },
      "outputs": [],
      "source": [
        "#@title Install TF Quant Finance\n",
        "!pip install tf-quant-finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ku46LPe6Jswv"
      },
      "source": [
        "### This notebook demonstrates the use of the TFF toolbox for performing common tasks related to interest rates. This includes computing the present values of cashflows for a collection of bonds, and for building and interpolating from rate curves, with an emphasis on:\n",
        "\n",
        "  * **Batching**: Tensorflow is vectorized out of the box. Tensorflow Finance (TFF) written to leverage this wherever possible. We illustrate the advantage of batching for computing forward rates."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "2nA2FSdTgcEM"
      },
      "outputs": [],
      "source": [
        "#@title Imports { display-mode: \"form\" }\n",
        "import datetime\n",
        "from dateutil.relativedelta import relativedelta\n",
        "import holidays\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
        "from pandas.tseries.offsets import CustomBusinessDay\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import time\n",
        "\n",
        " # TFF for Tensorflow Finance\n",
        "import tf_quant_finance as tff \n",
        "from tf_quant_finance import rates\n",
        "\n",
        "from IPython.core.pylabtools import figsize\n",
        "figsize(21, 14) # better graph size for Colab  \n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",\n",
        "                        category=FutureWarning)  # suppress printing warnings\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cy6Ndmf5bOwS"
      },
      "source": [
        "# Business Day Convention\n",
        "The pricing in Example 1 uses the *modified following business day convention* for the US for treasure payment dates. That is, if the coupon date falls on a weekend or holiday, it is paid on the following business day, unless said following business day falls into the next calendar month, in which case we go backwards to the nearest previous business day. It also provides functionality to generate regular coupon payments, before applying business day convention.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "s7I71pDssAyG"
      },
      "outputs": [],
      "source": [
        "#@title Implement Business Day Convention\n",
        "def get_coupon_dates(current_date, maturity_date, months = 6):\n",
        "  # Compute sequence of dates `months` apart starting at maturity_date,\n",
        "  # working backwards to last one after current_date. \n",
        "  cashflow_dates = []\n",
        "  date = maturity_date\n",
        "  while date \u003e= current_date:\n",
        "    cashflow_dates.append(date)\n",
        "    date = date + relativedelta(months=-months)\n",
        "  # Check if dates are on a US holiday or weekend\n",
        "  return pd.to_datetime(cashflow_dates).sort_values()\n",
        " \n",
        "def get_modified_following_date(dates):\n",
        "  # Get the modified folliwng business day for the US. \n",
        "  BDayUS = CustomBusinessDay(calendar=USFederalHolidayCalendar(), n = 1)       \n",
        "  def is_weekday(dates):\n",
        "    # Identify weekend days\n",
        "    return ~dates.weekday_name.isin(['Saturday', 'Sunday'])\n",
        "\n",
        "  def in_next_month(dates1, dates2): \n",
        "    return dates1.month == dates2.month - 1\n",
        "\n",
        "  def next_bus_day(dates):\n",
        "    # If next business day in a new month shift to previous business day\n",
        "    fwd = dates + BDayUS\n",
        "    return fwd.where(~in_next_month(dates, fwd), dates - BDayUS)\n",
        "\n",
        "  def payment_day(dates):\n",
        "    return dates.where(is_weekday(dates), next_bus_day(dates))\n",
        "  return payment_day(dates)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Zm4sRzOl19pq"
      },
      "source": [
        "## Example 1: **Cashflows in TFF**: Computing present values for a portfolio of bonds."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KsVBo68Fabgt"
      },
      "source": [
        " ### Coupon Bond Valuation\n",
        "Calculating the value of a coupon bond factors in the present value of annual or semi-annual coupon payments and the face value of the bond.\n",
        "\n",
        "The present value of expected cash flows is added to the present value (PV) of the face value of the bond as seen in the following formula:\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "PV(Bond) \u0026= PV(Coupons) + PV(FaceValue) \\\\\n",
        "\u0026= \\sum_t \\frac{C_t}{(1+i)^t} + \\frac{F}{(1+i)^T}\\\\\n",
        "\\end{align}\n",
        "$$\n",
        "Where \\\\\n",
        "$C_t$ are future coupon payments\n",
        "\n",
        "$i$ is the yield to maturity (or internal rate of return, IRR) of the bond. \n",
        "\n",
        "$F$ is the face value of the bond.\n",
        "\n",
        "$t$ is times at which the corresponding coupon payments occur.\n",
        "\n",
        "$T$ is the time to maturity of the bond.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oEcV_j3ZPSUm"
      },
      "source": [
        "# Example Data (US Treasury Bonds)\n",
        "The example below shows how to price a selection of US Treasury Bonds\n",
        "Source: https://www.wsj.com/market-data/bonds (Close of market on 20/09/2019)\n",
        "\n",
        "The data represent six US Treasuries:  \n",
        "*   2-Year Note (Coupon: 1.5%, Maturity: 30/09/2021)\n",
        "*   3-Year Note (Coupon: 1.5%, Maturity: 15/09/2022)\n",
        "*   5-Year Note (Coupon: 1.5%, Maturity: 30/09/2024)\n",
        "*   7-Year Note (Coupon: 1.625%, Maturity: 30/09/2026)\n",
        "*   10-Year Note (Coupon: 1.625%, Maturity: 15/08/2029)\n",
        "*   30-Year Bond (Coupon: 2.25%, Maturity: 15/08/2049)\n",
        "\n",
        "We use Modified Following day count convention (i.e. move to the next business day, \n",
        "unless it falls in a different month, in which case use the previous business day),\n",
        "and the US holidays in the Python `holidays` module."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 495,
          "status": "ok",
          "timestamp": 1570030572664,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "gZkUSC7hZb8T",
        "outputId": "c0d99911-ea83-4242-84c1-914847c58041"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Priced US Treasury Bonds:\n",
            "\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
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              "\u003c/table\u003e\n",
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            ],
            "text/plain": [
              "  present_date expiry_date  ... coupon_frequency  present_value\n",
              "0   2019-09-20  2021-09-30  ...              0.5     100.212446\n",
              "1   2019-09-20  2022-09-15  ...              0.5      99.446898\n",
              "2   2019-09-20  2024-09-30  ...              0.5      99.887903\n",
              "3   2019-09-20  2026-09-30  ...              0.5     100.139738\n",
              "4   2019-09-20  2029-08-15  ...              0.5      98.648447\n",
              "5   2019-09-20  2049-08-15  ...              0.5     984.092036\n",
              "\n",
              "[6 rows x 7 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title Pricing US Treasury Bonds\n",
        "dtype = np.float64\n",
        "exp_dates = ['\"2021-09-30\"', '\"2022-09-15\"', '\"2024-09-30\"', '\"2026-09-30\"', \n",
        "             '\"2029-08-15\"', '\"2049-08-15\"']\n",
        "us_bond_data = {\n",
        "    'present_date': [datetime.datetime.strptime('2019-09-20', '%Y-%m-%d').date()] * 6,\n",
        "    'expiry_date': [datetime.datetime.strptime(date, '\"%Y-%m-%d\"').date() for date in exp_dates], \n",
        "    'bond_type': ['2yr_note', '3yr_note', '5yr_note', '7yr_note', '10yr_note', \n",
        "                  '30yr_bond'],\n",
        "    'face_value': [100, 100, 100, 100, 100, 1000],\n",
        "    'coupon_rate': [0.015, 0.015, 0.015, 0.01625, 0.01625, 0.02250],\n",
        "    'coupon_frequency': [0.5] * 6\n",
        "}\n",
        "\n",
        "us_bond_data = pd.DataFrame.from_dict(us_bond_data)\n",
        "\n",
        "# Generate times of cashflows (using modified following business day convention)\n",
        "# for US federal holidays. \n",
        "payment_dates = list(map(get_coupon_dates, us_bond_data.present_date,\n",
        "                        us_bond_data.expiry_date))\n",
        "number_of_coupons = list(map(len, payment_dates)) # get number of coupons per bond\n",
        "payment_dates = np.concatenate(payment_dates, axis = 0)\n",
        "payment_dates_modified = get_modified_following_date(\n",
        "    pd.to_datetime(payment_dates))\n",
        "current_date = pd.Series(pd.to_datetime(us_bond_data.present_date[0])).repeat(len(payment_dates_modified))\n",
        "payment_times_days = (payment_dates_modified.values - current_date)\n",
        "times = payment_times_days.apply(lambda x: float(x.days) / 365) # Days to years\n",
        "\n",
        "\n",
        "# Generate actual cashflows.\n",
        "coupon_payments = (us_bond_data.face_value * us_bond_data.coupon_rate * \n",
        "                   us_bond_data.coupon_frequency)\n",
        "coupon_cashflows = np.repeat(coupon_payments, number_of_coupons)\n",
        "redemption_cashflows = np.zeros(np.sum(number_of_coupons))\n",
        "redemption_indexes = np.cumsum(number_of_coupons) - 1\n",
        "redemption_cashflows[redemption_indexes] = us_bond_data.face_value\n",
        "cashflows = np.array(coupon_cashflows + redemption_cashflows, dtype = dtype)\n",
        "# Compute groups for bond cashflows. \n",
        "groups = np.repeat(range(0, us_bond_data.shape[0]), number_of_coupons)\n",
        "\n",
        "# Bond Yield Curve \n",
        "# Yields ontained from https://www.wsj.com/market-data/bonds (as on 20/09/2019)\n",
        "tenor_curve = [2, 3, 5, 10, 30]\n",
        "rate_curve = [0.017419, 0.016885, 0.016614, 0.017849, 0.02321]\n",
        "days_to_maturity = (us_bond_data.expiry_date - us_bond_data.present_date)  \n",
        "years_to_maturity = list(days_to_maturity.apply(lambda x: float(x.days) / 365))\n",
        "\n",
        "\n",
        "# Linear Interpolation of curve get yields to maturity.\n",
        "rate_curve_interpolated = tff.math.interpolation.linear.interpolate(\n",
        "    years_to_maturity, tenor_curve, \n",
        "    rate_curve, dtype = np.float64)\n",
        "with tf.Session() as sess:\n",
        "  rate_curve_interpolated = sess.run(rate_curve_interpolated)\n",
        "\n",
        "# Create Tensorflow Graph using pv_from_yields in rates.\n",
        "present_values = rates.cashflows.pv_from_yields(cashflows, times, \n",
        "                                                rate_curve_interpolated,\n",
        "                                                groups) \n",
        "\n",
        "with tf.Session() as sess:\n",
        "  present_values = sess.run(present_values)\n",
        "\n",
        "us_bond_data['present_value'] = present_values\n",
        "\n",
        "print(\"Priced US Treasury Bonds:\")\n",
        "print('\\n')\n",
        "us_bond_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PpYxO8JklRRS"
      },
      "source": [
        "### Generating large bond portfolio \n",
        "To demonstrate scale we simulate a mix of bonds (of total number_of_bonds) with face values between min_face_value and max_face_value (in increments of 100), paying either semi-annual or annual coupons, with coupon rates of 2%, 4%, 6%, 8%, and 10%. We use the yield curve we used above for US treasury bonds. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1723,
          "status": "ok",
          "timestamp": 1570030623111,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "13_gS0ZIBw2x",
        "outputId": "250b7387-63d2-48f9-fb23-03ee6510b3fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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PkpCQAMCiRYtIS0vDaDTi4eHB1KlTiY2NBWDOnDls3boVT09PfHx8mDlzJtde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\nlClTujUGA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1VQUKBx48Zp0qRJ+s1vfuMZmz59uiTpH/7hHzR27Fjt2LHjvI85nfuu9LfX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\nmk+jAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 1152x864 with 4 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Create Bond Data\n",
        "number_of_bonds = 100000 #@param\n",
        "min_face_value = 100 \n",
        "max_face_value = 1000\n",
        "# Face values for bonds\n",
        "bond_face_values = range(min_face_value,\n",
        "                         max_face_value, 100)\n",
        "coupon_frequencies = [0.5, 1]\n",
        "coupon_rates = [0.02, 0.04, 0.06, 0.08, 0.10]\n",
        "\n",
        "# Range of bond maturities.\n",
        "bond_maturities = [1, 2, 3, 5, 7, 10, 15, 20, 30]\n",
        "\n",
        "# Create a mix of 100,000 bonds.\n",
        "large_bond_data = {\n",
        "    'face_value': np.random.choice(bond_face_values, number_of_bonds),\n",
        "    'coupon_frequency': np.random.choice(coupon_frequencies, number_of_bonds),\n",
        "    'coupon_rate': np.random.choice(coupon_rates, number_of_bonds, \n",
        "                                    p=[0.1, 0.2, 0.3, 0.3, 0.1]),\n",
        "    'maturity': np.random.choice(bond_maturities, number_of_bonds,\n",
        "                                 p=[0.1, 0.1, 0.1, 0.2, 0.3, 0.1, 0.05, \n",
        "                                    0.025, 0.025])\n",
        "    }\n",
        "\n",
        "large_bond_data = pd.DataFrame.from_dict(large_bond_data)\n",
        "\n",
        "# Rate curve interpolation\n",
        "curve_required_tenors2 = np.arange(0.5, 30.5, 0.5)\n",
        "\n",
        "rate_curve_interpolated2 = tff.math.interpolation.linear.interpolate(\n",
        "    curve_required_tenors2, tenor_curve, \n",
        "    rate_curve, dtype = np.float64)\n",
        "with tf.Session() as sess:\n",
        "  rate_curve_interpolated2 = sess.run(rate_curve_interpolated2)\n",
        "\n",
        "\n",
        "# Plot distribution of bonds by face value, coupon rate, and yield.\n",
        "plt.figure(figsize=(16,12))\n",
        "col_palette = sns.color_palette(\"Blues\")\n",
        "plt.subplot(2, 2, 1)\n",
        "# Plot Rate Curve\n",
        "sns.set()\n",
        "sns.lineplot(curve_required_tenors2, rate_curve_interpolated2,\n",
        "             color=col_palette[2])\n",
        "plt.title('Rate Curve', fontsize=14)\n",
        "plt.xlabel('Tenor', fontsize=12)\n",
        "plt.ylabel('Rate', fontsize=12)\n",
        "\n",
        "# Coupon rate distribution\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.set()\n",
        "sns.distplot(large_bond_data['coupon_rate'], kde=False,\n",
        "             color = col_palette[3], bins=5)\n",
        "plt.title('Bond Mix by Coupon Rate', fontsize=14)\n",
        "plt.xlabel('Coupon Rate', fontsize=12)\n",
        "plt.ylabel('Frequency', fontsize=12)\n",
        "\n",
        "# Nominal value distribution\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.set()\n",
        "sns.distplot(large_bond_data['face_value'], kde=False,\n",
        "             color = col_palette[4], bins=9)\n",
        "plt.title('Bond Mix by Nominal', fontsize=14)\n",
        "plt.xlabel('Nominal', fontsize=12)\n",
        "plt.ylabel('Frequency', fontsize=12)\n",
        "\n",
        "# Nominal value distribution\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.set()\n",
        "sns.distplot(large_bond_data['maturity'], kde=False,\n",
        "             color = col_palette[5], bins=9)\n",
        "plt.title('Bond Mix by Maturity', fontsize=14)\n",
        "plt.xlabel('Maturity', fontsize=12)\n",
        "plt.ylabel('Frequency', fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 2188,
          "status": "ok",
          "timestamp": 1570030879517,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "G8CJCZcEKCDc",
        "outputId": "e20dc783-89fb-4d92-9e44-f462ef0c20ab"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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bb5i+Li4uxqZNmzB27Fi88sorAGp/ftzc3DBnzhwcPHjQ7OdTqVRi7dq1kMtr44wgCJgx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\nIiJKMIZwIiIiIqIEYwgnIiIiIkqw/x9dIqlVnb3wcQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 864x576 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Compute the present value for portfolio of bonds\n",
        "dtype = np.float64\n",
        "tf.reset_default_graph()\n",
        "\n",
        "rate_curve_df = pd.DataFrame.from_dict({\n",
        "    'tenor': curve_required_tenors2,\n",
        "    'rate': rate_curve_interpolated2\n",
        "})\n",
        "\n",
        "# Create inputs (cashflows, times, groups) for `pv_from_yields`\n",
        "large_number_of_coupons = large_bond_data.maturity / large_bond_data.coupon_frequency\n",
        "large_number_of_coupons = large_number_of_coupons.astype(int)\n",
        "large_coupon_payments = (large_bond_data.face_value * large_bond_data.coupon_rate * \n",
        "                         large_bond_data.coupon_frequency)\n",
        "large_coupon_cashflows = np.repeat(large_coupon_payments, large_number_of_coupons)\n",
        "large_redemption_cashflows = np.zeros(np.sum(large_number_of_coupons))\n",
        "large_redemption_indexes = np.cumsum(large_number_of_coupons) - 1\n",
        "large_redemption_cashflows[large_redemption_indexes] = large_bond_data.face_value\n",
        "large_cashflows = np.array(large_coupon_cashflows + \n",
        "                           large_redemption_cashflows, dtype = dtype)\n",
        "\n",
        "# The times of the cashflows.\n",
        "large_times = list(map(np.arange, large_bond_data.coupon_frequency,\n",
        "             large_bond_data.maturity + large_bond_data.coupon_frequency,\n",
        "             large_bond_data.coupon_frequency))\n",
        "large_times = np.concatenate(large_times, axis = 0)\n",
        "large_groups = np.repeat(range(0, large_bond_data.shape[0]), \n",
        "                         large_number_of_coupons)\n",
        "\n",
        "# Create Tensorflow Graph using pv_from_yields in rates.\n",
        "present_values = rates.cashflows.pv_from_yields(large_cashflows, large_times, \n",
        "                                                rate_curve_interpolated2, \n",
        "                                                groups = large_groups)\n",
        "with tf.Session() as sess:\n",
        "  present_values = sess.run(present_values)\n",
        "\n",
        "  # Plot distribution of present values of portfolio of bonds.\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.set_context(\"talk\")\n",
        "col_palette = sns.color_palette(\"Blues\")\n",
        "sns.set()\n",
        "ax = sns.distplot(present_values, kde=True)\n",
        "plot_label = \"Present Value Disribution of {} Priced Bonds.\".format(number_of_bonds)\n",
        "plt.title(plot_label, fontsize=16)\n",
        "plt.xlabel('Present Value', fontsize=14)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZyR_nNOzx6VK"
      },
      "source": [
        "## Example 2: Compute forward rates given a set of zero rates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zKsOC65-2_Qt"
      },
      "source": [
        "Denote the price of a zero coupon bond maturing at time $t$ by $Z(t)$. Then the zero rate to time $t$ is defined as\n",
        "$$\n",
        "\\begin{equation*}\n",
        "    r(t) = - ln(Z(t)) / t\n",
        "\\end{equation*}\n",
        "$$\n",
        "\n",
        "This is the (continuously compounded) interest rate that applies between time $0$ and time $t$ as seen at time $0$. The forward rate between times $t1$ and $t2$ is defined as the interest rate that applies to the period $[t1, t2]$ as seen from today. Let $f(t1, t2) = -ln\\frac{Z(t2)}{Z(t1)}$, then it followes that\n",
        "$$\\begin{align}\n",
        "    \\\\\n",
        "    exp(-f(t1, t2)(t2-t1)) \u0026= Z(t2) / Z(t1)   \\\\\n",
        "    f(t1, t2) \u0026= - (ln Z(t2) - ln Z(t1)) / (t2 - t1) \\\\\n",
        "    f(t1, t2) \u0026= (t2 * r(t2) - t1 * r(t1)) / (t2 - t1) \\\\\\\\\n",
        "\\end{align}$$\n",
        "Given a sequence of increasing times $[t1, t2, ... tn]$ and the zero rates for those times, this function computes the forward rates that apply to the consecutive time intervals i.e. $[0, t1], [t1, t2], ... [t_{n-1}, tn]$ using the last equation above. Note that for the interval $[0, t1]$ the forward rate is the same as the zero rate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iZFCpmK657oZ"
      },
      "source": [
        "### Generating zero rates data\n",
        "We generate `num_zero_rates_bonds` sets of zero rates data with between 3 and 8 coupon payments at time points $[0.25, 0.5, 1, 1.5,2, 3, 5, 10]$, always starting at $0.25$ and then at subsequent time points, depending on the number of marked tenors. We generate zero rates as follows:\n",
        "\n",
        "\n",
        "1.   Randomly draw a rate in $[0,0.15]$ for the first tenor\n",
        "2.   Generate the rates for the subsequent tenors by incrementing the rate at the first tenor by a random draw from $[0, 0.02]$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "cSJagw3vyXZ8"
      },
      "outputs": [],
      "source": [
        "#@title Create Bond Data\n",
        "num_zero_rate_bonds = 100000 #@param\n",
        "num_tenors = [2, 3, 4, 5, 6, 7, 8, 10]\n",
        "marked_tenors = [0.25, 0.5, 1, 1.5, 2, 3, 5, 10, 20, 30]\n",
        "\n",
        "# Create a mix of `num_zero_rate_bonds` bonds.\n",
        "set_num_tenors = np.random.choice(num_tenors, num_zero_rate_bonds)\n",
        "\n",
        "def get_slice(n):\n",
        "  return marked_tenors[slice(n)]\n",
        "\n",
        "times = np.concatenate(list(map(get_slice, set_num_tenors)), axis = 0)\n",
        "# Set up a grouping argument for implementing batching. See\n",
        "# `forward_rates_from_yields` in tff.forwards.\n",
        "groups = np.repeat(range(0, num_zero_rate_bonds), set_num_tenors)\n",
        "\n",
        "# Construct Rate Curve to generate Zero Rates \n",
        "tf.reset_default_graph()\n",
        "curve_required_tenors3 = marked_tenors\n",
        "rate_curve_interpolated3 = tff.math.interpolation.linear.interpolate(\n",
        "    curve_required_tenors3, tenor_curve, \n",
        "    rate_curve, dtype = np.float64)\n",
        "\n",
        "with tf.Session() as sess:\n",
        "  rate_curve_interpolated3 = sess.run(rate_curve_interpolated3)\n",
        "  \n",
        "def get_rates(n):\n",
        "  # Perturb rate curve\n",
        "    rates = rate_curve_interpolated3[0:n]\n",
        "    rates =  rates + np.random.uniform(-0.0005, 0.0005, n)\n",
        "    return rates\n",
        "  \n",
        "rates = np.concatenate(list(map(get_rates, set_num_tenors)), axis = 0)\n",
        "\n",
        "zero_rate_data = {\n",
        "    'times': times,\n",
        "    'groups': groups,\n",
        "    'rates': rates\n",
        "    }\n",
        "\n",
        "zero_rate_data_df = pd.DataFrame.from_dict(zero_rate_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1421,
          "status": "ok",
          "timestamp": 1570030933881,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "ju89GTV2PEPA",
        "outputId": "a12c0b2b-80e7-4fd3-a262-a444c73934d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "wall time (with batching):  0.4285850524902344\n"
          ]
        },
        {
          "data": {
            "image/png": 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Q5+1ajC1jA1g0wF8N4gqiXBuRs7KyIiUlJUN6elCVHkA9KT1dp9NlWdbKysoo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\nIiIiIiIiSeZfr5zIiS43flcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 1008x432 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Compute forward rates for sets with different number of tenors of zero rates with batching.\n",
        "import tf_quant_finance.rates.forwards as forwards\n",
        "dtype = np.float64\n",
        "tf.reset_default_graph()\n",
        "\n",
        "forward_rates = forwards.forward_rates_from_yields(\n",
        "    rates, times, groups=groups, dtype=dtype)\n",
        "t = time.time()\n",
        "with tf.Session() as sess:\n",
        "  forward_rates = sess.run(forward_rates)\n",
        "time_batch = time.time() - t\n",
        "zero_rate_data_df['forward_rates'] = forward_rates\n",
        "\n",
        "# Plot forward rates for a random sample sets of zero rates\n",
        "sample_groups = np.random.choice(np.unique(groups), 5)\n",
        "plt.figure(figsize=(14,6))\n",
        "col_palette = sns.color_palette(\"Blues\", 5)\n",
        "mask = list(zero_rate_data_df.groups.isin(sample_groups))\n",
        "plot_data = zero_rate_data_df.iloc[mask]\n",
        "sns.set()\n",
        "sns.set_context(\"talk\")\n",
        "sns.lineplot(x='times', y='forward_rates', data=plot_data, \n",
        "             hue='groups',legend='full', palette=col_palette)\n",
        "\n",
        "plt.title('Sample of estimated forward rate sets', fontsize=16)\n",
        "plt.xlabel('Marked Tenor', fontsize=14)\n",
        "plt.ylabel('Forward Rate', fontsize=14)\n",
        "legend = plt.legend()\n",
        "legend.texts[0].set_text(\"Fwd Rate Group\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bnRzKSGxmbwM"
      },
      "source": [
        "### Forward rates (batching vs non-batching)\n",
        "Below we compare the computation of forward rates with and without batching. We see that computing the forward rates for 100 bonds is about 2.5 times slower than computing the forward rates for 100000 bonds with batching."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 7210,
          "status": "ok",
          "timestamp": 1570030992322,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "jxzOOZF_gs5-",
        "outputId": "e4eb3de0-d85c-4e6d-a78e-f617c9a3ee9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "wall time (non-batch):  4.39509391784668\n",
            "Pricing 100 bonds without batching is 10.3 times slower than \n",
            "  pricing 100000 bonds with batching.\n"
          ]
        }
      ],
      "source": [
        "#@title Compare forward rate computation: batching vs non-batching.\n",
        "num_zero_rate_bonds2 = 100\n",
        "num_tenors = [2, 3, 4, 5, 6, 7, 8, 10]\n",
        "marked_tenors = [0.25, 0.5, 1, 1.5, 2, 3, 5, 10, 20, 30]\n",
        "\n",
        "# Create a mix of 100,000 bonds.\n",
        "set_num_tenors = np.random.choice(num_tenors, num_zero_rate_bonds2)\n",
        "\n",
        "def get_slice(n):\n",
        "  # Function to get marked tenors for a bond with 'n' tenors. \n",
        "  return marked_tenors[slice(n)]\n",
        "\n",
        "times = np.concatenate(list(map(get_slice, set_num_tenors)), axis = 0)\n",
        "# Set up a grouping argument for implementing batching. See\n",
        "# `forward_rates_from_yields` in tff.forwards.\n",
        "groups = np.repeat(range(0, num_zero_rate_bonds2), set_num_tenors)\n",
        "\n",
        "# Construct Rate Curve to generate Zero Rates \n",
        "tf.reset_default_graph()\n",
        "curve_required_tenors3 = marked_tenors\n",
        "rate_curve_interpolated3 = tff.math.interpolation.linear.interpolate(\n",
        "    curve_required_tenors3, tenor_curve, \n",
        "    rate_curve, dtype = np.float64)\n",
        "\n",
        "with tf.Session() as sess:\n",
        "  rate_curve_interpolated3 = sess.run(rate_curve_interpolated3)\n",
        "  \n",
        "def get_rates(n):\n",
        "  # Perturb rate curve\n",
        "    rates = rate_curve_interpolated3[0:n]\n",
        "    rates =  rates + np.random.uniform(-0.0005, 0.0005, n)\n",
        "    return rates\n",
        "  \n",
        "rates = np.concatenate(list(map(get_rates, set_num_tenors)), axis = 0)\n",
        "\n",
        "# Non-batch.\n",
        "tf.reset_default_graph()\n",
        "time_non_batch = 0\n",
        "with tf.Session() as sess:\n",
        "  for group in np.unique(groups):\n",
        "    forward_rates_non_batch = forwards.forward_rates_from_yields(\n",
        "        rates[groups == group], times[groups == group], dtype=dtype)\n",
        "    t = time.time() \n",
        "    forward_rates_non_batch = sess.run(forward_rates_non_batch)\n",
        "    time_non_batch += time.time() - t\n",
        "print('wall time to price {} options without batching: '.format(num_zero_rate_bonds2), time_non_batch)\n",
        "print('wall time to price {} options with batching: '.format(num_zero_rate_bonds), time_batch)\n",
        "output_string = \"\"\"Pricing {} bonds without batching is {} times slower than \n",
        "  pricing {} bonds with batching.\"\"\"\n",
        "\n",
        "print(output_string.format(num_zero_rate_bonds2, \n",
        "                           round(time_non_batch/time_batch, 1), \n",
        "                           num_zero_rate_bonds))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hPHhWz0xedlI"
      },
      "source": [
        "### Pricing 100 bonds without batching is about 10 times slower than pricing 100000 bonds with batching."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oy9lAtkgPyHC"
      },
      "source": [
        "## Example 3: Constructing a bond discount curve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tk4DgYGpQfzW"
      },
      "source": [
        "Building discount curves is a core problem in mathematical finance. Discount curves are built using the available market data in liquidly traded rates\n",
        "products. These include bonds, swaps, forward rate agreements (FRAs) or eurodollar futures contracts. \n",
        "\n",
        "Here we show how to build a bond discount rate curve. A discount curve is a function of time which gives the interest rate that applies to a unit of currency deposited today for a period of  time $t$. The traded price of bonds implicitly contains the market view on the discount rates. The purpose of discount curve construction is to extract this information. \n",
        "\n",
        "The algorithm we use here here is based on the Monotone Convex Interpolation method described by Hagan and West (2006, 2008).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "9HIAjlDCosM6"
      },
      "outputs": [],
      "source": [
        "# @title Create bond data\n",
        "# The following example demonstrates the usage by building the implied curve\n",
        "# from four coupon bearing bonds.\n",
        "\n",
        "dtype=np.float64\n",
        "# These need to be sorted by expiry time.\n",
        "cashflow_times = [\n",
        "                  np.array([0.25, 0.5, 0.75, 1.0], dtype=dtype),\n",
        "                  np.array([0.5, 1.0, 1.5, 2.0], dtype=dtype),\n",
        "                  np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0], dtype=dtype),\n",
        "                  np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0], \n",
        "                           dtype=dtype)\n",
        "                  ]\n",
        "cashflows = [\n",
        "    # 1 year bond with 5% three monthly coupon.\n",
        "    np.array([12.5, 12.5, 12.5, 1012.5], dtype=dtype),\n",
        "    # 2 year bond with 6% semi-annual coupon.\n",
        "    np.array([30, 30, 30, 1030], dtype=dtype),\n",
        "    # 3 year bond with 8% semi-annual coupon.\n",
        "    np.array([40, 40, 40, 40, 40, 1040], dtype=dtype),\n",
        "    # 4 year bond with 3% semi-annual coupon.\n",
        "    np.array([15, 15, 15, 15, 15, 15, 15, 1015], dtype=dtype)\n",
        "]\n",
        "# The present values of the above cashflows.\n",
        "pvs = np.array([\n",
        "                999.68155223943393, 1022.322872470043, 1093.9894418810143,\n",
        "                934.20885689015677\n",
        "], dtype=dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1456,
          "status": "ok",
          "timestamp": 1570031025461,
          "user": {
            "displayName": "Wiesner Vos",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mDrzSmBQPAJuP5OSGQPYwcBQKx8AphQNUIzX_g27g=s64",
            "userId": "04015980978702098465"
          },
          "user_tz": -60
        },
        "id": "2d2zsKXURRYI",
        "outputId": "e8072679-51e2-47ef-b4b1-177a8d7ef8f3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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jm4/WHODTrw+Smp5t6fBEREREpBwp1ULutddeY9GiRfTs2ZMJEyZgNBp59tln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\nImIzfwDwK5fpjp8HYQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 1008x432 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Build and plot the bond curve \n",
        "tf.reset_default_graph()\n",
        "from tf_quant_finance.rates import hagan_west\n",
        "\n",
        "results = hagan_west.bond_curve(cashflows, cashflow_times, pvs)\n",
        "\n",
        "with tf.Session() as sess:\n",
        "  results = sess.run(results)\n",
        "\n",
        "# Plot Rate Curve\n",
        "plt.figure(figsize=(14,6))\n",
        "col_palette = sns.color_palette(\"Blues\", 2)\n",
        "sns.set()\n",
        "sns.set_context(\"talk\")\n",
        "sns.lineplot(x=results.times, y=results.discount_rates, palette=col_palette)\n",
        "plt.title('Estimated Discount Rates', fontsize=16)\n",
        "plt.xlabel('Marked Tenor', fontsize=14)\n",
        "plt.ylabel('Discount Rate', fontsize=14)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "",
        "kind": "private"
      },
      "name": "Cashflows_Rate_Curves.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
